GetXEO vs Peec AI

Peec AI answers one question very well: is your brand showing up in AI answers today. GetXEO answers that too, and then does the four things that decide it. Finding the questions buyers actually ask, building a connected set of pages instead of isolated posts, writing every block so a model can lift it, and making sure engines can reach and parse the result.

Arjun Shenoy

Co-Founder and CPO at GetXEO

Evaluates AI visibility tooling as part of the product role, and has run this category comparison across ten platforms.

31 August 2026 · 11 min read · 2,314 words · 9 cited sources

About the teamLinkedIn

Peec AI is strongest on the final row. It presents itself as AI search analytics rather than as a content system, and the product matches that description. The four rows above it are where a citation is won or lost, and each one can fail on its own and take the others down with it.

You set up a workspace with a brand name and a domain, and Peec AI suggests prompts matched to real customer search intent. You can add your own, tag them by persona or funnel stage, and pick the models to run them against.

Three numbers come back. Visibility is the share of chats where the brand is mentioned. Position is where it lands inside the answer. Sentiment is how favorably it is described. Every prompt runs once every 24 hours on each model you select, which turns AI visibility into a trend line rather than a snapshot taken whenever somebody remembered to look.

The citation work is the strong part. Peec AI separates a brand mention, where an answer names you, from a source citation, where your page fed the answer whether or not you were named. It tracks both at domain and URL level with frequency, then turns the pattern into recommendations: get a profile on the review site that keeps appearing, join the discussion thread engines keep quoting, place a story where editorial domains are cited.

Data leaves the product easily. There is a CSV export, a Looker Studio connector, a REST API, and an MCP server for pulling visibility data into tools like Claude.

GetXEO starts by reading the brand. It crawls the domain, classifies every URL, and builds a brand profile with mission, audience and tone that every downstream writer reads. Then it derives the question universe, and curates twenty five queries for each of the three pillars, each mapped back to the keyword or question it came from.

Those queries produce one composite XEO score. The dashboard carries AEO, GEO and SEO sub scores, model citation rates, and the change since the last scan. AEO polls answer engines. GEO covers AI summarized search and weights paragraph position, so a first paragraph mention counts for more. SEO tracks the classic Google result page alongside AI Overviews and knowledge panels.

Then the audits. Three of them, one per pillar, rolling into a composite audit score with a page by page heat map of issues and a prioritized action list. Technical fixes arrive as a per page checklist a developer can work through without a marketer translating.

The output is content. Fifty blogs a month on the single workspace plan, produced as one interconnected mesh rather than a pile of posts, sequenced on a twelve week calendar that also carries refreshes and fixes. Plain English brand rules constrain every piece the system writes.

This is the difference that compounds, and it is settled before either product has measured any AI visibility at all.

A prompt list is seeded from search volume and from suggestions, which describe how people phrase things into a search box. Most B2B buyers now run part of the buying process through an engine, and more than half use one to compare vendors. Our position is that what they describe to it is a situation rather than a category, so a question set derived from modeling that behavior is a different artifact and points at different pages. That is a design judgment, and the page states it as one.

Both products then measure the same way, so the measurement is not what separates them. No amount of daily sampling repairs a question set built from the wrong starting point. It simply reports, very precisely, on the wrong questions.

• A genuine trend line. Every tracked prompt runs every 24 hours on every selected model, so month over month movement is comparable rather than a reading taken whenever somebody remembered to look
• Two kinds of citation, kept apart. A brand mention and a source citation are different events, and Peec AI reports both at domain and URL level with frequency, which is the honest way to read whether engines trust your pages
• It goes where your data already lives. A CSV export, which the product page advertises without tying it to a plan, a Looker Studio connector on the higher plans, plus a REST API and an MCP integration, so AI visibility numbers reach the reporting dashboard a team already has open
• Regions do not cost extra. Countries and languages are not metered, because pricing follows prompts and models rather than geography, which matters to any brand selling across borders

The traction is real too. TechCrunch reported in May 2026 that Peec AI had crossed ten million dollars in annualized revenue and opened a New York office.

On a few operational rows Peec AI publishes a figure and GetXEO does not.

Customizable means GetXEO publishes no fixed figure for that row and sets it per workspace, so a buyer should ask rather than assume. Not published means the capability does not appear in the feature catalog at all, which is a larger gap and worth naming as one. GetXEO scopes a market with a workspace rather than a country setting, so the two products count geography differently and the row is not a like for like. Peec AI now renders its plan prices in the browser, read on 29 August 2026 at eighty, two hundred and five, and four hundred and twenty dollars a month on annual billing, for fifty, a hundred and fifty and three hundred and fifty prompts.

• The whole chain in one place. Question set, mesh, craft, technical audit and measurement together, so no step falls to a second product or to nobody in particular
• One score across three layers. AEO, GEO and SEO roll into a single figure with model citation rates and the change since the last scan, instead of one layer measured here and the rest matched up by hand
• Delivery is verified rather than assumed. The GEO audit checks entity declarations, Organization schema, semantic HTML and knowledge graph signals, and the fixes arrive as a list a developer can work through
• Output that connects. A mesh on a sequenced calendar, so authority accumulates across the whole set rather than starting again at every URL

One limit is worth knowing before committing. GetXEO publishes a weekly scan rhythm and runs scans on demand, which is a slower default than a daily one.

The edge is not one feature. It is how much of the chain each product covers. Peec AI tells you whether you are being cited, clearly and often. GetXEO tells you that and performs the four things that determine the answer.

The questions. Modeling buyer behavior surfaces the situations people describe to an engine, which is a different set from the one search volume suggests. Everything downstream inherits that choice.

The structure. A mesh lets each page carry the others. A lone post has to win by itself, which is why scattered publishing achieves so little however good any single post is.

The craft. An engine lifts a passage and shows it with the page removed. Pages carrying figures, citations and quotes get named far more often, and pages get their highest citation rates in their first week live, and both come down to how the page was written. It matters more than it looks, because almost all buyers who research with AI check what it tells them before trusting it, so the cited page is the one they land on.

The delivery. Recommendations to earn a review profile or a press mention are useful, and they are a different lever from whether an engine can fetch and parse your own site. Each AI operator crawls under its own user agent, and OpenAI says a robots change is picked up in about a day, so this is both the cheapest fix available and the one nobody owns.

And the reach of the number. One score spanning answer engines, AI summarized search and Google itself. about half of B2B buyers now start vendor research inside AI tools, but it does not stay there, and a score that stops at the AI platforms stops halfway.

GetXEO fits a marketing team that publishes often and is not being cited for it. The second sign is that nobody owns the technical layer. Peec AI fits a team that already writes well and wants one clean number across several markets.

All were published or last updated within the past twelve months. A competitor page appears only as a record of that vendor’s own published terms.

1. Machine Relations, AI search citation factors research
2. Machine Relations, how B2B buyers research vendors with AI
3. MarketScale, on where B2B software discovery starts in 2026
4. MarketScale, on the TrustRadius finding that buyers fact check AI
5. OpenAI, the crawler and user agent documentation
6. Cloudflare, the AI crawler bot reference naming each operator, category and user agent
7. TechCrunch, on Peec crossing ten million dollars in annualized revenue, May 2026
8. Peec AI, the published product page, read 27 August 2026
9. Peec AI, the published pricing page and plan comparison, read 27 August 2026

1. What Peec AI monitors and what GetXEO acts on
2. Moving from Peec AI to GetXEO
3. GetXEO vs Peec AI for growing marketing teams
4. GetXEO vs Profound
5. GetXEO vs AirOps